The Taylor Rule and Forecast Intervals for Exchange Rates

In this paper, we examine the Meese–Rogoff puzzle from a different perspective: out‐of‐sample interval forecasting. While most studies in the literature focus on point forecasts, we apply semiparametric interval forecasting to a group of exchange rate models. Forecast intervals for 10 OECD exchange rates are generated and the performance of the empirical exchange rate models are compared with the random walk. Our contribution is twofold. First, we find that in general, exchange rate models generate tighter forecast intervals than the random walk, given that their intervals cover out‐of‐sample exchange rate realizations equally well. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting distributions of exchange rates. We also find that the benchmark Taylor rule model performs better than the monetary, PPP and forward premium models, and its advantages are more pronounced at longer horizons. Second, the bootstrap inference framework proposed in this paper for forecast interval evaluation can be applied in a broader context, such as inflation forecasting.     

Alternative modeling for long term risk

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root of time method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations using different models and distributions. By comparing the estimations of two risk measures, value at risk and expected shortfall, with different models and innovations at short-, median- and long-term horizon, we find that the best model varies with the forecasting horizon and that the generalized Pareto distribution gives the most conservative estimations with all the models at all the horizons. The empirical results show that the square root method underestimates risk at long horizons and our approach is more competitive for risk estimation over a long term.     

Nonlinear exchange rate predictability

We study whether the nonlinear behavior of the real exchange rate can help us account for the lack of predictability of the nominal exchange rate. We construct a smooth nonlinear error-correction model that allows us to test the hypotheses of nonlinear predictability of the nominal exchange rate and nonlinear behavior on the real exchange rate in the context of a fully specified cointegrated system. Using a panel of 19 countries and three numeraires, we find evidence of nonlinear predictability of the nominal exchange rate and of nonlinear mean reversion of the real exchange rate. Out-of-sample Theil’s U -statistics show a higher forecast precision of the nonlinear model than the one obtained with a random walk specification. Although the robustness of the out-of-sample results over different forecast windows is somewhat limited, we are able to obtain significant predictability gains—from a parsimonious structural model with PPP fundamentals—even at short-run horizons.     

The impact of uncertainty on professional exchange rate forecasts

This paper analyzes the role of uncertainty on both exchange rate expectations and forecast errors of professionals for four major currencies based on survey data provided by FX4casts. We consider economic policy, macroeconomic, and financial uncertainty as well as disagreement among CPI inflation forecasters to account for different dimensions of uncertainty. Based on a Bayesian VAR approach, we observe that uncertainty effects on forecast errors of professionals turn out to be more significant compared to the adjustment of exchange rate expectations. Our findings are robust to different forecasting horizons and point to an unpredictable link between exchange rates and fundamentals. Furthermore, we illustrate the importance of considering common unpredictable components for a large number of variables. We also focus on the post-crisis period and the relationship between uncertainty and disagreement among exchange rate forecasters and identify a strong relationship between them.     

A new approach to forecasting exchange rates

Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several potential problems associated with broad price indexes, such as the consumer price index used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon. These high-frequency forecasts could be appealing to those who want up-to-date exchange-rate forecasts. Finally, as our forecasts are obtained through a simulation procedure, estimation uncertainty is made explicit in our framework that provides the entire distribution of exchange rates, not just a single point estimate. Using exchange rates of six major currencies to illustrate the approach, we compare the Big Mac forecasts with those derived from a random walk and the CPI and find some support for our approach, especially at longer term horizons.     

Combining long memory and level shifts in modelling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.     

Forecasting EUR–USD implied volatility: The case of intraday data

This study models and forecasts the evolution of intraday implied volatility on an underlying EUR–USD exchange rate for a number of maturities. To our knowledge we are the first to employ high frequency data in this context. This allows the construction of forecasting models that can attempt to exploit intraday seasonalities such as overnight effects. Results show that implied volatility is predictable at shorter horizons, within a given day and across the term structure. Moreover, at the conventional daily frequency, intraday seasonality effects can be used to augment the forecasting power of models. The type of inefficiency revealed suggests potentially profitable trading models.     

Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

We propose a multivariate nonparametric technique for generatingreliable short-term historical yield curve scenarios and confidenceintervals. The approach is based on a Functional Gradient Descent(FGD) estimation of the conditional mean vector and covariancematrix of a multivariate interest rate series. It is computationallyfeasible in large dimensions and it can account for nonlinearitiesin the dependence of interest rates at all available maturities.Based on FGD we apply filtered historical simulation to computereliable out-of-sample yield curve scenarios and confidenceintervals. We back-test our methodology on daily USD bond datafor forecasting horizons from 1 to 10 days. Based on severalstatistical performance measures we find significant evidenceof a higher predictive power of our method when compared toscenarios generating techniques based on (i) factor analysis,(ii) a multivariate CCC-GARCH model, or (iii) an exponentialsmoothing covariances estimator as in the RiskMetrics^TM approach.     

Estimating exchange rate responsiveness to shocks

The goal of this paper is to examine the importance of permanent and transitory shocks using a more efficient trend-cycle decomposition of the real exchange rate series. Our main contribution is that in measuring the impact of shocks, we not only impose common trend restrictions but also common cycle restrictions. We later confirm, through a post sample forecasting exercise, the efficiency gains from imposing common cycle restrictions. Our results indicate that permanent shocks are responsible for the bulk of the real exchange rate variations for Japan, Italy, Germany, France, and the UK vis-à-vis the US dollar over short horizons. For Canada, however, transitory shocks are dominant over the short horizon. In sum, while for Japan, France, and Italy, around 15% of the variation in real exchange rate is due to transitory shocks, for Canada, Germany and the UK, over 25% of the variations over the short horizon are due to transitory shocks. Thus, we claim that the role of transitory shocks should not be ignored.     

Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion. The extensions decompose conditional variance into a short-term and a long-term component. The latter utilizes mixed-data sampling or a heterogeneous autoregressive structure, avoiding parameter proliferation otherwise incurred by using the classical ARMA structures embedded in the REGARCH. The proposed models are dynamically complete, facilitating multi-period forecasting. A thorough empirical investigation with an exchange-traded fund that tracks the S&P500 Index and 20 individual stocks shows that our models better capture the dependency structure of volatility. This leads to substantial improvements in empirical fit and predictive ability at both short and long horizons relative to the original REGARCH. A volatility-timing trading strategy shows that capturing volatility persistence yields substantial utility gains for a mean–variance investor at longer investment horizons.     

Forecasting the price of gold using dynamic model averaging

We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.     

STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.     

An investigation of customer order flow in the foreign exchange market

This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new, and the largest, proprietary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make several contributions. Firstly, we investigate the extent to which customer order flow can help to explain exchange rate movements over and above the influence of macro-economic variables. Secondly, we address the issue of whether order flows contain (private) information which explain exchange rates changes. Thirdly, we look at the usefulness of order flow in forecasting exchange rate movements at longer horizons than those generally considered in the micro-structure literature. Finally we address the question of whether the out-of-sample exchange rate forecasts generated by order flows can be employed profitably in the foreign exchange markets.     

Forecasting exchange rates out-of-sample with panel methods and real-time data

This paper evaluates out-of-sample exchange rate forecasting with Purchasing Power Parity (PPP) and Taylor rule fundamentals for 9 OECD countries vis-à-vis the U.S. dollar over the period from 1973:Q1 to 2009:Q1 at short and long horizons. In contrast with previous work, which reports “forecasts” using revised data, I construct a quarterly real-time dataset that incorporates only the information available to market participants when the forecasts were made. Using bootstrapped out-of-sample test statistics, the exchange rate model with Taylor rule fundamentals performs better at the one-quarter horizon and panel estimation is not able to improve its performance. The PPP model, however, forecasts better at the 16-quarter horizon and its performance increases in panel framework. The results are in accord with previous research on PPP and Taylor rule models.     

A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio.     

Exchange rate expectations and the pricing of Chinese cross-listed stocks

I show that the price discounts of Chinese cross-listed stocks (American Depositary Receipts (ADRs) and H-shares) to their underlying A-shares indicate the expected yuan/US dollar exchange rate. The forecasting models reveal that ADR and H-share discounts predict exchange rate changes more accurately than the random walk and forward exchange rates, particularly at long forecast horizons. Using panel estimations, I find that ADR and H-share investors form their exchange rate expectations according to standard exchange rate theories such as the Harrod-Balassa-Samuelson effect, the risk of competitive devaluations, relative purchasing power parity, uncovered interest rate parity, and the risk of currency crisis.     

Forecasting government bond yields with large Bayesian vector autoregressions

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing on the US, we provide an extensive study on the forecasting performance of the proposed model relative to most of the existing alternative specifications. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also consider alternative measures based on trading schemes and portfolio allocation. We extensively check the robustness of our results, using different datasets and Monte Carlo simulations. We find that the proposed BVAR approach produces competitive forecasts, systematically more accurate than random walk forecasts, even though the gains are small.     

Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation

The paper is concerned with time series modelling of foreign exchange rate of an important emerging economy, viz., India, with due consideration to possible sources of misspecification of the conditional mean like serial correlation, parameter instability, omitted time series variables and nonlinear dependences. Since structural change is pervasive in economic time series relationships, the paper first studies this aspect of the exchange rate series in detail and finds the existence of four structural breaks. Accordingly, the entire sample period is divided into five sub-periods of stable parameters each, and then the appropriate mean specification for each of these sub-periods is determined by incorporating functions of recursive residuals. Thereafter, the GARCH and EGARCH models are considered to capture the volatility contained in the data. The estimated models thus obtained suggest that return on Indian exchange rate series is marked by instabilities and that the appropriate volatility model is EGARCH. Further, out-of-sample forecasting performance of the model has been studied by standard forecasting criteria, and then compared with that of an AR model only to find that the findings are quite favorable for the former.      

Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion

A stylized fact of U.S. inflation dynamics is one of extreme persistence and possible unit root behavior. If so, the implications for macroeconomics and monetary policy are somewhat unpalatable. Our econometric analysis proposes a parsimonious univariate representation of the inflation process for the last 60 years, the nonlinear exponential smooth autoregressive. The empirical results confirm a number of the key features such as global stationarity, local unit root behavior, and lower persistence in the post-1983 period than in the pre-1983 period. We compare the forecasting ability of our model with that of competing univariate models and find that the nonlinear model outperforms the linear autoregressive model in the pre-1983 period and the random walk in the post-1983 period at short horizons.     

Contagion as a domino effect in global stock markets

This paper shows that stock market contagion occurs as a domino effect, where confined local crashes evolve into more widespread crashes. Using a novel framework based on ordered logit regressions we model the occurrence of local, regional and global crashes as a function of their past occurrences and financial variables. We find significant evidence that global crashes do not occur abruptly but are preceded by local and regional crashes. Besides this form of contagion, interdependence shows up by the effect of interest rates, bond returns and stock market volatility on crash probabilities. When it comes to forecasting global crashes, our model outperforms a binomial model for global crashes only.